Bayesian community detection in Stochastic Block Models: a comparison of marginal samplers ​

Community detection is a fundamental problem in the field of complex networks. A network is said to have a community structure if the nodes of the network can be easily clustered into sets of nodes such that each set of nodes is densely connected internally, and nodes from different sets are rarely connected. Various approaches have been proposed for community detection; among them, there are model-based approaches. In this thesis, we present the Bayesian Stochastic Block model, introduce two approaches to making inferences about the community structures and compare their performance under the simulated data.